Automatic Emergency Braking Research Articles

Review on Lane Detection Techniques

Abstract—Lane detection in current automobiles is extremely popular; it has become an inseparable part of the vehicle’s set, as it is considered indispensable nowadays to prevent an accident and limit the risk of injury or even death on the road. This review article addresses some of the different systems of line measuring features and characteristics, strengths, and weaknesses. This article starts by reviewing the literature and showing the different methods, methods, and techniques that are employed in the research line. Then, it continues with the description of the main elements of the search line, such as camera, image processing algorithms, and alerts, regarding the variety of functions that may be embodied in a line lane search, such as lane departure warning, cruise control, and automatic emergency braking. Compared the advantages and disadvantages of various camera, radar-based and lidar-based line search methods and set a comparison of accuracy, reliability, cost, and performance in different driving styles. In this report, it also discusses challenges and limitations of line calling like bad weather conditions, road signs, and poor performance, along with the risks and disadvantages of over-reliance on lane detection of the vehicles. Index Terms—Lane detection, reliability, robustness, semantic splitting, effectiveness

Research on an Automatic Emergency Braking Control Method Based on Coordinated Wheel Deflection

Research on an Automatic Emergency Braking Control Method Based on Coordinated Wheel Deflection

Research on Occupant Integrated Safety Under Automatic Emergency Braking on Lower Limb Injuries During Car Accident

Research on Occupant Integrated Safety Under Automatic Emergency Braking on Lower Limb Injuries During Car Accident

Road target recognition based on radar range-Doppler spectrum with GS – ResNet

ABSTRACT Road target recognition ability of automotive millimetre-wave (mmWave) radar is crucial in areas such as autonomous driving, advanced driver assistance systems (ADAS), and automated emergency braking systems (AEBS). Current mmWave radar systems are primarily utilized for speed, distance, and angle measurements, with limited capability or unsatisfactory performance in target classification. In response to this limitation, this paper proposes a novel method for road target recognition, using radar range-Doppler spectrum as input data and introducing a global attention mechanism and SCConv module. First, the range-Doppler spectrum is processed by the clutter removal algorithm block to remove background clutter and vibration interference. Second, considering the small features and susceptibility to noise interference in the range-Doppler spectrum, we have devised a deep residual network based on a global attention mechanism, significantly enhancing the model’s accuracy in range-Doppler spectrum classification. Finally, we introduce the SCConv module and improve the downsampling module in ResNet to enhance the model’s classification accuracy. Experimental results demonstrate that the model achieves an average accuracy of 99.79% in classifying six types of road targets, significantly outperforming other methods. This research is of significant importance in advancing the understanding of road traffic conditions by autonomous driving systems and enhancing system safety.

Assessing advanced driver assistance systems in police vehicles under demanding conditions

Advanced driver-assistance systems (ADAS) are technologies that can enhance drivers’ safety by relieving them from some driving related activities. However, police driving conditions and demands are different from those of civilian drivers. The objective of this study was to assess the impact of ADAS such as forward collision warning (FCW), automatic emergency braking (AEB), and blind spot monitoring (BSM) on police officers’ driving performance, workload, and trust in vehicle safety to provide personalised solutions for police vehicles. A driving simulation study was conducted with 18 police officers. ADAS use was assessed under various driving conditions and while officers were engaged in non-driving related tasks. Findings suggested that the FCW and AEB systems improved officers’ driving performance, while the BSM system had limited effectiveness due to low salience. ADAS were beneficial under normal driving conditions and when officers were using in-vehicle technology; however, they did not help officers in pursuit conditions.

A note on Tesla's revised safety report crash rates

Between June 2018 and December 2023, Tesla released quarterly safety reports citing average miles between crashes for Tesla vehicles. Prior to March 2021, crash rates were categorized as (1) with their SAE Level 2 automated driving system Autopilot engaged, (2) without Autopilot but with active safety features such as automatic emergency braking, and (3) without Autopilot and without active safety features. In January 2023, Tesla revised past reports to reflect their new categories of with and without Autopilot engaged, in addition to making small adjustments based on recently discovered double counting of reports and excluding previously recorded crashes that did not meet their thresholds of airbag or active safety restraint activation. The revisions are heavily biased towards no-active-safety-features—a surprising result given prior research showing that drivers predominantly keep most active safety features enabled. As Tesla's safety reports represent the only national source of Level 2 advanced driver assistance system crash rates, clarification of their methods is essential for researchers and regulators. This note describes the changes and considers possible explanations for the discrepancies.

Generalization of cut-in pre-crash scenarios for autonomous vehicles based on accident data

The utilization of high-risk test cases constitutes an effective approach to enhance the safety testing of autonomous vehicles (AVs) and enhance their efficiency. This research paper presents a derivation of 2052 high-hazard pre-crash scenarios for testing autonomous driving, which were based on 23 high-hazard cut-in accident scenarios from the National Automobile Accident In-Depth Investigation System (NAIS) through combining importance sampling and combined testing methods. Compared to the direct combination of the original distribution after sampling, the proposed method has a 2.92 times higher crash rate of 69.32% for the test case set in this paper. It also has a 5.8 times higher rate of triggering Automatic Emergency Braking (AEB), improving hazardous scenario coverage. Using the proposed method, the generated parameters of the cut-in accident scenario test set were compared with those of the cut-in test scenarios included in existing Chinese autonomous driving test protocols and standards. The velocity of the ego-vehicle obtained using the proposed method matched those in the existing protocols, whereas the velocity, time gap, and time to collision of the target vehicle were significantly lower than those existing protocols indicating scenarios obtained from accident data can enrich the selection of testing scenarios for autonomous driving.

Real-time testing of AI enabled automatic emergency braking system for ADAS vehicle using 3D point cloud and precise depth information

At the forefront of automobile safety technology, Automatic Emergency Braking (AEB) represents a major advancement in collision avoidance systems. The cutting-edge technology provides an additional layer of security at pivotal times, making it a vital part of the changing landscape of vehicle safety. This research introduces an effective system for automatic emergency braking in ADAS-equipped or Autonomous vehicles using a combination of 3d lidar and stereo vision camera for a swift and robust system in the vehicle that is faster than human drivers in cases of unexpected emergencies. Utilizing the power of clustering algorithms on 3d point clouds and state-of-the-art computer vision algorithms on an RGB image mapped to a depth frame from the stereo vision camera, the system as a whole provides a comprehensive system adding to the safety of the vehicle and the passengers. Further, the efficiency of the system is studied based on various parameters. The data from an external inertial measurement unit is also utilized to derive results that support the claims of the study. The system has been developed and implemented on a passenger car that has been modified into an electric vehicle and further tested in real-world traffic conditions in autonomous driving mode. The findings of the study were having exceptionally good precision in split-second decision-making in emergency maneuvers.

Evaluation of comfort zone boundary based automated emergency braking algorithms for car‐to‐powered‐two‐wheeler crashes in China

AbstractCrashes between cars and powered two‐wheelers (PTWs: motorcycles, scooters, and e‐bikes) are a safety concern; as a result, developing car safety systems that protect PTW riders is essential. While the pre‐crash protection system automated emergency braking (AEB) has been shown to avoid and mitigate injuries for car‐to‐car, car‐to‐cyclist, and car‐to‐pedestrian crashes, much is still unknown about its effectiveness in car‐to‐PTW crashes. Further, the characteristics of the crashes that remain after the introduction of such systems in traffic are also largely unknown. This study estimates the crash avoidance and injury risk reduction performance of six different PTW‐AEB algorithms that were virtually applied to reconstructed car‐to‐PTW pre‐crash kinematics extracted from a Chinese in‐depth crash database. Five of the algorithms include combinations of drivers’ and PTW riders’ comfort zone boundaries for braking and steering, while the sixth is a traditional AEB. Results show that the average safety performance of the algorithms using only the driver's comfort zone boundaries is higher than that of the traditional AEB algorithm. All algorithms resulted in similar distributions of impact speed and impact locations, which means that in‐crash protection systems likely can be made less complex, not having to consider differences in AEB algorithm design among car manufacturers.

SDC-Net++: End-to-End Crash Detection and Action Control for Self-Driving Car Deep-IoT-Based System.

Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles' bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird's eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively.

Pedestrian automatic emergency braking responses to a stationary or crossing adult mannequin during the day and night

Objective Automatic emergency braking systems with pedestrian detection (PAEB) are effective at preventing pedestrian crashes, but the safety benefits are not observed at night. This study used the Insurance Institute for Highway Safety (IIHS) PAEB test data to characterize PAEB responses in different lighting conditions and for different rated systems. Methods Data from 6,919 IIHS PAEB tests were retrieved from IIHS databases. Invalid trials, trials without AEB, and trials with outliers were removed leaving 5,894 trials from 212 model year 2018 to 2023 vehicles for analysis. PAEB responses were characterized by computing the time-to-collision (TTC) of forward collision warning (FCW) and AEB; brake threat number (BTN); mean deceleration; maximum deceleration; and maximum jerk. A linear mixed-effects model was used to predict each dependent measure with scenario (crossing adult, stationary adult), speed, rating (superior, basic/advanced), lighting (day, night with high beams, night with low beams), and their interactions. Vehicle was included as a random effect. A Bonferroni correction was applied to maintain a family-wise type-1 error rate of 0.05 across 138 total hypothesis tests. Results PAEB system warnings were later and automatic braking occurred later as speed increased at night with low beams but changed little at night with high beams and during the day (p < 0.0004). BTN increased more rapidly as speed increased at night with low beams compared with high beams and during the day (p < 0.0004). Based on the BTN model, on average, PAEB systems can brake to avoid the adult mannequin (BTN < 1) when closing speed is less than 67 km/h during the day and at night with high beams but only when closing speed is less than 49 km/h at night with low beams. Superior-rated PAEB systems warned and braked earlier compared with basic/advanced-rated systems (p < 0.0004). Conclusions Increased lighting from high beams made nighttime performance resemble daytime performance in controlled testing. Increasing output from vehicle low beams, increasing the use of high beams, and enhancing overhead lighting around crosswalks and pedestrian areas are all methods for increasing lighting, improving pedestrian conspicuity, and enhancing PAEB performance to prevent pedestrian crashes.

Anti-low angle resolution: Some adaptive improvements in anchor-based object detection algorithm for automotive radar target detection

Automotive radar has extensive and well-established applications in advanced driver assistance systems (ADAS). It has been the most reliable and preferred sensor for functions such as adaptive cruise control and automatic emergency braking. Nevertheless, the limited angle resolution of traditional millimeter-wave radar data reduces its effectiveness in autonomous driving systems (ADS). Our study presents some adaptive improvements in anchor-based object detection algorithm framework to address the inaccuracies in target detection and positioning resulting from the limited angle resolution of millimeter-wave radar data. Our model utilizes the Range-Angle (RA) and Range-Doppler (RD) views of a RAD cube as inputs. An anchor assignment strategy - Max Intersection over Union (IoU) assigner is used to generate more positive samples than before, enabling the model to learn more about the relevant features of targets. We propose a multi-detection box fusion Non-Maximum Suppression (NMS) mechanism to fuse prediction results with varying confidence scores, thus improving the accuracy of target detection positions by addressing the issue of target box position deviation caused by low angle resolution. In addition, an actual distance loss function is designed to constrain the regularity that the size of the bounding box is inversely proportional to the distance between the target and the radar. Based on experimental results, the proposed method increases the mean average precision (mAP) by approximately 10% compared to the baseline method.

Research on AEB-P Control Strategy Considering Braking Process Comfort

A control approach for the pedestrian-oriented automobile automatic emergency braking system (AEB-P) that takes driving comfort into account is suggested in an effort to further optimize the AEB-P system's control algorithm. Firstly, for the AEB-P assessment standard of C-NCAP, a braking safety distance model that takes the driver's comfort into account during braking is created. The next step was to construct a hierarchical controller using PID and MPC principles. Lastly, CarSim and Matlab/Simulink are used to build and validate the test scenarios and control methods. The simulation results demonstrate that both ride comfort and driving safety can be guaranteed by the suggested control technique.

Integrating security in hazard analysis using STPA-Sec and GSPN: A case study of automatic emergency braking system

Hazard analysis is a vital step in developing intelligent connected vehicles, aiming to eliminate or control hazards in the initial stages of system development and to provide theoretical support for the system’s safety design. However, conventional hazard analysis methods, such as Fault Tree Analysis and Failure Mode and Effects Analysis, suffer from two shortcomings: they do not account for the impact of cybersecurity factors on system safety and do not provide sufficient quantification of hazard scenarios. To this end, we propose a quantifiable hazard analysis method with security consideration, which integrates System Theoretic Process Analysis for Security (STPA-Sec) and Generalized Stochastic Petri Net (GSPN), supporting the extraction, modeling, and quantification of hazards. Specifically, we employ STPA-Sec for qualitative analysis to identify causal scenarios, safety requirements, security requirements, and the corresponding mitigations. Then, based on the identified causal scenarios, a GSPN model is established to quantify system-level hazards. A case study on a real open-source test vehicle demonstrates that the proposed method not only offers a comprehensive analysis of hazards but also provides a quantitative assessment. Comparative assessments suggest that the proposed method exhibits an advantage in terms of analysis processes (integrating security) and results (quantification).

Design of a Multidimensional Intelligent Safety Driving Monitoring System Based on Machine Vision

This study introduces a multidimensional driving safety monitoring system that integrates millimeter-wave radar and an Arduino Uno-based alcohol sensor. The system aims to enhance driving safety and prevent accidents by real-time monitoring and analysis of the driving environment, driver's condition, and vehicle dynamics using machine vision technology. Utilizing millimeter-wave radar, the system precisely detects obstacles and pedestrians around the vehicle, facilitating efficient distance and speed measurements to support automatic emergency braking and collision warnings. Additionally, the Arduino Uno-based alcohol sensor monitors the driver's breath for alcohol concentration, ensuring the driver is in a suitable state for driving. Combined with machine vision technology, the system further analyzes video stream data from inside and outside the cockpit, monitoring driver behavior and external traffic conditions to enhance the identification of potential risks. The success of this research illustrates the potential of combining machine vision with advanced sensor technologies, offering new insights for the development of comprehensive driving assistance systems.

EMERGENCY BRAKING SYSTEM IN FOUR-WHEELER

Automatic Emergency Braking (AEB) is a braking system which helps to apply brakes automatically under the condition of emergency and helps to mitigate the severity of a crash. This paper deals with the study of designing and working of automatic emergency braking system using the various fundamentals of mechanical and electronics engineering popularly known as ‘Mechatronics’. In this system Ultrasonic Sensors with the combined use of Stereo Cameras will detect an obstacle in front of the vehicle and it will tell us the relative distance between the obstacle and the vehicle. The ECU will then judge if an accident is likely to happen or not and the brakes will be applied automatically under this system. Key Words: Heat exchanger, Perforated rings, Reynolds number, Overall heat transfer coefficient.

Injury risk curves to guide safe speed limits on Swedish roads using German crash data supplemented with estimated non-injury crashes

Vision Zero postulates that no one should be killed or seriously injured in road traffic; therefore, it is necessary to define evidence-based speed limits to mitigate impact severity. The overall aims to guide the definition of safe speeds limits by establishing relations between impact speed and the risk of at-least-moderate (MAIS2+) and at-least-severe (MAIS3+) injuries for car occupants in frontal and side crashes in Sweden. As Swedish in-depth data are unavailable, the first objective was to assess the applicability of German In-depth Accident Study (GIDAS) data to Sweden. The second was to create unconditional injury risk curves (risk of injury given involvement in any crash), rather than risk curves conditional on the GIDAS sampling criterion of suspected-injury crashes. Thirdly, we compared the unconditional and conditional risk curves to quantify the practical implications of this methodological choice. Finally, we provide an example to demonstrate how injury risk curves facilitate the definition of safe, evidence-based speed limits in Sweden. Characteristics important for the injury outcome were similar between GIDAS and Swedish data; therefore, the injury risk curves using German GIDAS data are applicable to Sweden. The regression models yielded the following results for unconditional injury risk curves: 10 % MAIS2+ at 25 km/h impact speed for frontal head-on crashes, 20 km/h for frontal car-to-object crashes, 55 km/h in far-side crashes, and 45 km/h in near-side crashes. A 10 % MAIS3+ risk was reached between 70 and 75 km/h for all crash types. Conditional injury risk curves gave substantially different results; the 10 % MAIS3+ risk in near-side crashes was 140 km/h, twice the unconditional value. For example, if a 10 % MAIS3+ risk was acceptable, treating remaining uncertainty conservatively, assuming compliance with speed limits and that Automated Emergency Braking takes 20 km/h of the travel speed before impact in longitudinal traffic, the safe speed limit for car occupants on most Swedish roads would be 80 km/h and 60 km/h in intersections.

The effectiveness of Q6 and PIPER 6-year-old models on quantification of the change of child sitting posture with AEB and its impact on child’s injures in frontal crash

Objective Automatic Emergency Braking (AEB) has a direct impact on the effectiveness of the restraint systems in providing protection toward child occupants. The objective is to evaluate the effectiveness of Q6 and PIPER 6-year-old models in predicting the kinematic responses of child models, and further to quantify and analyze the child injuries during a frontal crash with and without AEB. Methods The finite element model of a booster seat has been validated through a full vehicle test. Based on the validated finite element model, two sled test finite element models for the rear seat booster seat with Q6 and PIPER 6-year-old models were constructed. AEB condition was imposed on above the two models and the kinematic responses of sitting posture including head, neck and chest have been compared in detail. The peak head displacement and neck curvature of Q6 dummy and PIPER 6-year-old models have been compared with the test data from child volunteers. Based on the child model with better predictive capability for kinematic response under AEB, child injuries were evaluated and analyzed for the 50 km/h frontal crash test with and without AEB. Last, this study discussed the effects of internal neck and chest structure difference between Q6 and PIPER 6-year-old models on child kinematic response and the injury risks. Results Under AEB condition, PIPER 6-year-old model has higher head displacement and lower trunk displacement than Q6 dummy model, and the peak head displacement and neck curvature of PIPER 6-year-old model are similar to the test data of child volunteers. During the 50 km/h frontal crash simulation with pre-crash AEB, the HIC15, Head acceleration 3 ms, Nij decrease 43.7%, 19.6% and 28.8%, respectively and the chest deflection increases 15.5% compared to the simulation without AEB. Conclusions This study shows that PIPER 6-year-old model is more suitable for the quantification of sitting posture change under AEB due to its higher biofidelity. The pre-crash AEB can substantially reduce the head, neck injuries. But it also increases the chest injury due to the chest pre-compression. Future efforts are recommended to lower the child chest injury by integrating AEB with active pre-tensioning seatbelts.

DESIGN OF IOT BASED TRICYCLE

The rapid advancement in Internet of Things (IoT) technology has opened up numerous opportunities for innovation across various industries. In the transportation sector, IoT has the potential to revolutionize conventional vehicles into smart, connected modes of transportation. This project focuses on the design of an IoT-based electric tricycle, aimed at providing a sustainable and efficient mode of personal transportation.The electric tricycle integrates IoT technology to enhance its functionality and user experience. Key features include real-time monitoring of vehicle parameters such as speed, battery status, and location, enabling remote tracking and management through a dedicated mobile application. Additionally, the tricycle incorporates smart safety features such as collision detection and automatic emergency braking, enhancing rider safety on the road.The design phase involves the selection of appropriate components and the integration of IoT modules, sensors, and actuators into the tricycle framework. The fabrication process encompasses building the chassis, integrating the electric propulsion system, and assembling the IoT components. Emphasis is placed on ensuring durability, reliability, and energy efficiency throughout the construction process.Overall, the project aims to contribute to the advancement of sustainable transportation solutions by leveraging IoT technology to create an innovative, eco-friendly electric tricycle. Through this endeavor, we envision promoting the adoption of electric vehicles and facilitating a more connected and efficient urban mobility ecosystemTop of Form Keywords: IoT-based electric tricycle, Sustainable transportation, Connected vehicles, Electric vehicle innovation, Real-time monitoring, Safety features, Fabrication process, Integration of IoT technology, User experience, Smart mobility solution

Analysis and prospects of automobile intelligent assisted driving characteristics based on FPGA technology

This article provides a comprehensive exploration of the pivotal role that field-programmable gate arrays (FPGAs) play in the advancement of autonomous driving technology. FPGAs, which made their debut in the early 1980s, have emerged as a crucial component in this field, owing to their robust parallel processing capabilities, real-time data analysis capabilities, and exceptional customizability. With the ever-increasing demand for autonomous driving solutions, the adoption of FPGAs has become indispensable in meeting the requirements for high-speed data processing and instantaneous response times. Within these pages, we delve into the significant role that FPGAs assume in elevating intelligent driving systems, offering a deep dive into this subject through meticulous case studies and technical insights. This article casts a spotlight on several compelling instances where FPGAs shine, notably in adaptive cruise systems, obstacle recognition, automatic emergency braking, and intelligent parking assistance, achieved through seamless integration with YOLO technology. These real-world examples serve to underscore the pivotal role that FPGAs play in ensuring road safety and propelling technological advancement in the realm of autonomous driving.